Structure Prediction of RNA Loops with a Probabilistic Approach

PLoS Comput Biol. 2016 Aug 5;12(8):e1005032. doi: 10.1371/journal.pcbi.1005032. eCollection 2016 Aug.

Abstract

The knowledge of the tertiary structure of RNA loops is important for understanding their functions. In this work we develop an efficient approach named RNApps, specifically designed for predicting the tertiary structure of RNA loops, including hairpin loops, internal loops, and multi-way junction loops. It includes a probabilistic coarse-grained RNA model, an all-atom statistical energy function, a sequential Monte Carlo growth algorithm, and a simulated annealing procedure. The approach is tested with a dataset including nine RNA loops, a 23S ribosomal RNA, and a large dataset containing 876 RNAs. The performance is evaluated and compared with a homology modeling based predictor and an ab initio predictor. It is found that RNApps has comparable performance with the former one and outdoes the latter in terms of structure predictions. The approach holds great promise for accurate and efficient RNA tertiary structure prediction.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology / methods*
  • Models, Molecular
  • Models, Statistical*
  • Monte Carlo Method
  • Nucleic Acid Conformation
  • RNA / chemistry*
  • RNA / ultrastructure*

Substances

  • RNA

Grants and funding

This work was funded by National Natural Science Foundation of China (http://nsfc.pubmed.cn) (Grant No. 11274157 to JZ, 11574132 to WL and 11174133 to JW). WW acknowledges the support from National Basic Research and Development Program of China (http://www.973.gov.cn/) (Grant No. 2012CB921502 and 2013CB834100). The authors also acknowledge the support from the PAPD project of Jiangsu higher education institutions (http://jsycw.ec.js.edu.cn/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.